EnsembleModelFitResult

class openstef_meta.models.ensemble_forecasting_model.EnsembleModelFitResult(**data: Any) → None

Bases: ModelFitResult

Fit result for EnsembleForecastingModel.

Extends ModelFitResult with per-forecaster details. The base class fields (input_dataset, metrics_*, etc.) represent the combiner’s fit results.

Parameters:

data (Any)

forecaster_fit_results: dict[str, ModelFitResult]

metrics_to_flat_dict() → dict[str, float]

Flatten all split metrics into a single dict for logging.

Keys are prefixed with full_, train_, val_, test_ respectively. Subclasses with child results (e.g. per-forecaster) should override to include them.

Return type:

dict[str, float]

Returns:

Flat mapping of metric names to values.

property component_fit_results: dict[str, ModelFitResult]

Per-component fit results (e.g. per-forecaster in an ensemble).

Returns:

Empty dict by default; ensemble subclasses override.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'protected_namespaces': (), 'ser_json_inf_nan': 'null'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].